Heat processing is one of the most efficient strategies used in food industry to improve quality and prolong shelf life. However, conventional processing methods such as microwave heating, burning charcoal treatment, boiling, and frying are energy-inefficient and often lead to inferior product quality. Superheated steam (SHS) is an innovative technology that offers many potential benefits to industry and is increasingly used in food industry. Compared to conventional processing methods, SHS holds higher heat transfer coefficients, which can reduce microorganisms on surface of foodstuffs efficiently. Additionally, SHS generates a low oxygen environment, which prevents lipid oxidation and harmful compounds generation. Furthermore, SHS can facilitate development of desired product quality, such as protein denaturation with functional characteristics, proper starch gelatinization, and can also reduce nutrient loss, and improve the physicochemical properties of foodstuffs. The current work provides a comprehensive review of the impact of SHS on the nutritional, physicochemical, and safety properties of various foodstuffs including meat, fruits, and vegetables, cereals, etc. Additionally, it also provides food manufacturers and researchers with basic knowledge and practical techniques for SHS processing of foodstuffs, which may improve the current scope of SHS and transfer current food systems to a healthy and sustainable one.

The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis methods may lack the capability of modeling complex nonlinear relations between spectral information and nutrition content. Therefore, we initiated this study to explore the feasibility of integrating deep learning with NIR-HSI for food nutrition estimation. Inspired by reinforcement learning, we proposed OptmWave, an approach that can perform modeling and wavelength selection simultaneously. It achieved the highest accuracy on our constructed scrambled eggs with tomatoes dataset, with a determination coefficient of 0.9913 and a root mean square error (RMSE) of 0.3548. The interpretability of our selection results was confirmed through spectral analysis, validating the feasibility of deep learning-based NIR-HSI in food nutrition estimation.